Add elliptical slice sampling algorithm

src/Turing.jl

@@ -214,6 +214,7 @@ export  @model,                 # modelling
         @sampler,
 
         MH,                     # classic sampling
+        ESS,
         Gibbs,
 
         HMC,                    # Hamiltonian-like sampling

src/core/RandomVariables.jl

@@ -34,6 +34,7 @@ export  VarName,
         resetlogp!,
         set_retained_vns_del_by_spl!,
         is_flagged,
+        set_flag!,
         unset_flag!,
         setgid!,
         updategid!,
@@ -495,11 +496,10 @@ end
 end
 
 # Get all vns of variables belonging to spl
-_getvns(vi::UntypedVarInfo, spl::AbstractSampler) = view(vi.metadata.vns, _getidcs(vi, spl))
-function _getvns(vi::TypedVarInfo, spl::AbstractSampler)
-    # Get a NamedTuple of the indices of variables belonging to `spl`, one entry for each symbol
-    idcs = _getidcs(vi, spl)
-    return _getvns(vi.metadata, idcs)
+_getvns(vi::AbstractVarInfo, spl::Sampler) = _getvns(vi, spl.selector, Val(getspace(spl)))
+_getvns(vi::UntypedVarInfo, s::Selector, space) = view(vi.metadata.vns, _getidcs(vi, s, space))
+function _getvns(vi::TypedVarInfo, s::Selector, space)
+    return _getvns(vi.metadata, _getidcs(vi, s, space))
 end
 # Get a NamedTuple for all the `vns` of indices `idcs`, one entry for each symbol
 @generated function _getvns(metadata, idcs::NamedTuple{names}) where {names}
@@ -525,7 +525,7 @@ end
     #end
 end
 # Get the index (in vals) ranges of all the vns of variables belonging to selector `s` in `space`
-@inline function _getranges(vi::AbstractVarInfo, s::Selector, space = Val(()))
+@inline function _getranges(vi::AbstractVarInfo, s::Selector, space)
     return _getranges(vi, _getidcs(vi, s, space))
 end
 @inline function _getranges(vi::UntypedVarInfo, idcs::Vector{Int})

src/inference/Inference.jl

@@ -3,7 +3,7 @@ module Inference
 using ..Core, ..Core.RandomVariables, ..Utilities
 using ..Core.RandomVariables: Metadata, _tail, VarInfo, TypedVarInfo, 
     islinked, invlink!, getlogp, tonamedtuple, VarName, getsym, vectorize, 
-    settrans!
+    settrans!, _getvns, getdist
 using ..Core: split_var_str
 using Distributions, Libtask, Bijectors
 using ProgressMeter, LinearAlgebra
@@ -13,7 +13,7 @@ using ..Turing: Model, runmodel!, Turing,
     Selector, AbstractSamplerState, DefaultContext, PriorContext,
     LikelihoodContext, MiniBatchContext, NamedDist, NoDist
 using StatsFuns: logsumexp
-using Random: GLOBAL_RNG, AbstractRNG
+using Random: GLOBAL_RNG, AbstractRNG, randexp
 using AbstractMCMC
 
 import MCMCChains: Chains
@@ -33,6 +33,7 @@ export  InferenceAlgorithm,
         SampleFromUniform,
         SampleFromPrior,
         MH,
+        ESS,
         Gibbs,      # classic sampling
         HMC,
         SGLD,
@@ -274,8 +275,8 @@ function _params_to_array(ts::Vector{T}, spl::Sampler) where {T<:AbstractTransit
         end
         push!(dicts, d)
     end
-    
-    # Convert the set to an ordered vector so the parameter ordering 
+
+    # Convert the set to an ordered vector so the parameter ordering
     # is deterministic.
     ordered_names = collect(names)
     vals = Matrix{Union{Real, Missing}}(undef, length(ts), length(ordered_names))
@@ -486,6 +487,7 @@ end
 # Concrete algorithm implementations. #
 #######################################
 
+include("ess.jl")
 include("hmc.jl")
 include("mh.jl")
 include("is.jl")
@@ -498,7 +500,7 @@ include("../contrib/inference/AdvancedSMCExtensions.jl")
 # Typing tools #
 ################
 
-for alg in (:SMC, :PG, :PMMH, :IPMCMC, :MH, :IS, :Gibbs)
+for alg in (:SMC, :PG, :PMMH, :IPMCMC, :MH, :IS, :ESS, :Gibbs)
     @eval getspace(::$alg{space}) where {space} = space
 end
 for alg in (:HMC, :HMCDA, :NUTS, :SGLD, :SGHMC)
@@ -635,7 +637,14 @@ function assume(
     vi::VarInfo,
 )
     if haskey(vi, vn)
+        if is_flagged(vi, vn, "del")
+            unset_flag!(vi, vn, "del")
+            r = spl isa SampleFromUniform ? init(dist) : rand(dist)
+            vi[vn] = vectorize(dist, r)
+            setorder!(vi, vn, vi.num_produce)
+        else
         r = vi[vn]
+        end
     else
         r = isa(spl, SampleFromUniform) ? init(dist) : rand(dist)
         push!(vi, vn, r, dist, spl)
@@ -792,9 +801,19 @@ function get_and_set_val!(
 )
     n = length(vns)
     if haskey(vi, vns[1])
+        if is_flagged(vi, vns[1], "del")
+            unset_flag!(vi, vns[1], "del")
+            r = spl isa SampleFromUniform ? init(dist, n) : rand(dist, n)
+            for i in 1:n
+                vn = vns[i]
+                vi[vn] = vectorize(dist, r[:, i])
+                setorder!(vi, vn, vi.num_produce)
+            end
+        else
         r = vi[vns]
+        end
     else
-        r = isa(spl, SampleFromUniform) ? init(dist, n) : rand(dist, n)
+        r = spl isa SampleFromUniform ? init(dist, n) : rand(dist, n)
         for i in 1:n
             push!(vi, vns[i], r[:,i], dist, spl)
         end
@@ -808,9 +827,21 @@ function get_and_set_val!(
     spl::AbstractSampler,
 )
     if haskey(vi, vns[1])
+        if is_flagged(vi, vns[1], "del")
+            unset_flag!(vi, vns[1], "del")
+            f = (vn, dist) -> spl isa SampleFromUniform ? init(dist) : rand(dist)
+            r = f.(vns, dists)
+            for i in eachindex(vns)
+                vn = vns[i]
+                dist = dists isa AbstractArray ? dists[i] : dists
+                vi[vn] = vectorize(dist, r[i])
+                setorder!(vi, vn, vi.num_produce)
+            end
+        else
         r = reshape(vi[vec(vns)], size(vns))
+        end
     else
-        f(vn, dist) = isa(spl, SampleFromUniform) ? init(dist) : rand(dist)
+        f = (vn, dist) -> spl isa SampleFromUniform ? init(dist) : rand(dist)
         r = f.(vns, dists)
         push!.(Ref(vi), vns, r, dists, Ref(spl))
     end

src/inference/ess.jl

@@ -0,0 +1,146 @@
+"""
+    ESS
+
+Elliptical slice sampling algorithm.
+
+# Examples
+```jldoctest; setup = :(Random.seed!(1))
+julia> @model gdemo(x) = begin
+           m ~ Normal()
+           x ~ Normal(m, 0.5)
+       end
+gdemo (generic function with 2 methods)
+
+julia> sample(gdemo(1.0), ESS(), 1_000) |> mean
+Mean
+
+│ Row │ parameters │ mean     │
+│     │ Symbol     │ Float64  │
+├─────┼────────────┼──────────┤
+│ 1   │ m          │ 0.824853 │
+```
+"""
+struct ESS{space} <: InferenceAlgorithm end
+
+ESS() = ESS{()}()
+ESS(space::Symbol) = ESS{(space,)}()
+
+mutable struct ESSState{V<:VarInfo} <: AbstractSamplerState
+    vi::V
+end
+
+function Sampler(alg::ESS, model::Model, s::Selector)
+    # sanity check
+    vi = VarInfo(model)
+    space = getspace(alg)
+    vns = _getvns(vi, s, Val(space))
+    length(vns) == 1 ||
+        error("[ESS] does only support one variable ($(length(vns)) variables specified)")
+    for vn in vns[1]
+        dist = getdist(vi, vn)
+        isgaussian(dist) ||
+            error("[ESS] only supports Gaussian prior distributions")
+    end
+
+    state = ESSState(vi)
+    info = Dict{Symbol, Any}()
+
+    return Sampler(alg, info, s, state)
+end
+
+isgaussian(dist) = false
+isgaussian(::Normal) = true
+isgaussian(::NormalCanon) = true
+isgaussian(::AbstractMvNormal) = true
+
+# always accept in the first step
+function step!(::AbstractRNG, model::Model, spl::Sampler{<:ESS}, ::Integer; kwargs...)
+    return Transition(spl)
+end
+
+function step!(
+    rng::AbstractRNG,
+    model::Model,
+    spl::Sampler{<:ESS},
+    ::Integer,
+    ::Transition;
+    kwargs...
+)
+    # obtain mean of distribution
+    vi = spl.state.vi
+    vns = _getvns(vi, spl)
+    μ = mapreduce(vcat, vns[1]) do vn
+        dist = getdist(vi, vn)
+        vectorize(dist, mean(dist))
+    end
+
+    # obtain previous sample
+    f = vi[spl]
+
+    # recompute log-likelihood in logp
+    if spl.selector.tag !== :default
+        runmodel!(model, vi, spl)
+    end
+
+    # sample log-likelihood threshold for the next sample
+    threshold = getlogp(vi) - randexp(rng)
+
+    # sample from the prior
+    set_flag!(vi, vns[1][1], "del")
+    runmodel!(model, vi, spl)
+    ν = vi[spl]
+
+    # sample initial angle
+    θ = 2 * π * rand(rng)
+    θmin = θ - 2 * π
+    θmax = θ
+
+    while true
+        # compute proposal and apply correction for distributions with nonzero mean
+        sinθ, cosθ = sincos(θ)
+        a = 1 - (sinθ + cosθ)
+        vi[spl] = @. f * cosθ + ν * sinθ + μ * a
+
+        # recompute log-likelihood and check if threshold is reached
+        runmodel!(model, vi, spl)
+        if getlogp(vi) > threshold
+            break
+        end
+
+        # shrink the bracket
+        if θ < 0
+            θmin = θ
+        else
+            θmax = θ
+        end
+
+        # sample new angle
+        θ = θmin + rand(rng) * (θmax - θmin)
+    end
+
+    return Transition(spl)
+end
+
+function tilde(ctx::DefaultContext, sampler::Sampler{<:ESS}, right, vn::VarName, inds, vi)
+    if vn in getspace(sampler)
+        return tilde(LikelihoodContext(), SampleFromPrior(), right, vn, inds, vi)
+    else
+        return tilde(ctx, SampleFromPrior(), right, vn, inds, vi)
+    end
+end
+
+function tilde(ctx::DefaultContext, sampler::Sampler{<:ESS}, right, left, vi)
+    return tilde(ctx, SampleFromPrior(), right, left, vi)
+end
+
+function dot_tilde(ctx::DefaultContext, sampler::Sampler{<:ESS}, right, left, vn::VarName, inds, vi)
+    if vn in getspace(sampler)
+        return dot_tilde(LikelihoodContext(), SampleFromPrior(), right, left, vn, inds, vi)
+    else
+        return dot_tilde(ctx, SampleFromPrior(), right, left, vn, inds, vi)
+    end
+end
+
+function dot_tilde(ctx::DefaultContext, sampler::Sampler{<:ESS}, right, left, vi)
+    return dot_tilde(ctx, SampleFromPrior(), right, left, vi)
+end

src/inference/gibbs.jl

@@ -2,7 +2,7 @@
 ### Gibbs samplers / compositional samplers.
 ###
 
-const GibbsComponent = Union{Hamiltonian,MH,PG}
+const GibbsComponent = Union{Hamiltonian,MH,ESS,PG}
 
 """
     Gibbs(algs...)
@@ -150,7 +150,7 @@ function step!(
         # Uncomment when developing thinning functionality.
         # Retrieve symbol to store this subsample.
         # symbol_id = Symbol(local_spl.selector.gid)
-        
+
         # # Store the subsample.
         # spl.state.subsamples[symbol_id][] = trans
 

test/inference/ess.jl

@@ -0,0 +1,58 @@
+using Turing, Random, Test
+
+dir = splitdir(splitdir(pathof(Turing))[1])[1]
+include(dir*"/test/test_utils/AllUtils.jl")
+
+@testset "ESS" begin
+    @model demo(x) = begin
+        m ~ Normal()
+        x ~ Normal(m, 0.5)
+    end
+    demo_default = demo(1.0)
+
+    @model demodot(x) = begin
+        m = Vector{Float64}(undef, 2)
+        @. m ~ Normal()
+        x ~ Normal(m[2], 0.5)
+    end
+    demodot_default = demodot(1.0)
+
+    @turing_testset "ESS constructor" begin
+        Random.seed!(0)
+        N = 500
+        s1 = ESS()
+        s2 = ESS(:m)
+        s3 = Gibbs(ESS(:m), MH(:s))
+
+        c1 = sample(demo_default, s1, N)
+        c2 = sample(demo_default, s2, N)
+        c3 = sample(demodot_default, s1, N)
+        c4 = sample(demodot_default, s2, N)
+        c5 = sample(gdemo_default, s3, N)
+    end
+
+    @numerical_testset "ESS inference" begin
+        Random.seed!(1)
+        chain = sample(demo_default, ESS(), 5_000)
+        check_numerical(chain, [:m], [0.8], atol = 0.1)
+
+        Random.seed!(1)
+        chain = sample(demodot_default, ESS(), 5_000)
+        check_numerical(chain, ["m[1]", "m[2]"], [0.0, 0.8], atol = 0.1)
+
+        Random.seed!(100)
+        alg = Gibbs(
+            CSMC(15, :s),
+            ESS(:m))
+        chain = sample(gdemo(1.5, 2.0), alg, 10_000)
+        check_numerical(chain, [:s, :m], [49/24, 7/6], atol=0.1)
+
+        # MoGtest
+        Random.seed!(125)
+        alg = Gibbs(
+            CSMC(15, :z1, :z2, :z3, :z4),
+            ESS(:mu1), ESS(:mu2))
+        chain = sample(MoGtest_default, alg, 6000)
+        check_MoGtest_default(chain, atol = 0.1)
+    end
+end